Apache Kafka and Amazon SQS compete in the messaging and streaming category. Kafka has an upper hand in advanced streaming capabilities and scalability, while SQS offers easier AWS integration and user-friendliness.
Features: Apache Kafka supports robust features such as high scalability through data partitioning, efficient streaming capabilities for real-time data handling, and resilience in message retention, beneficial for distributed processing with tools like Apache Spark. Amazon SQS, integrated within the AWS ecosystem, is highlighted for reliable queuing, FIFO capabilities, and a user-friendly interface facilitating easy message management through visibility timeouts.
Room for Improvement: Apache Kafka could improve with better monitoring tools and simplification of deployment to reduce its dependency on ZooKeeper. It has a steep learning curve and would benefit from enhanced documentation. Conversely, Amazon SQS could address limitations in message size and cost management for large-scale operations. Enhanced visibility in message handling and better ecosystem interaction are also suggested improvements.
Ease of Deployment and Customer Service: Apache Kafka offers flexibility to deploy in various environments, including on-premises and cloud models, but comes with associated complexity and reliance on community support unless through third-party vendors like Confluent. Amazon SQS is primarily adopted in public clouds, especially AWS, offering ease of deployment with structured support from AWS, making it accessible for users needing robust support and simpler setups.
Pricing and ROI: Apache Kafka is open-source, providing cost-effective usage for enterprises managing their implementations, offering significant ROI, although expertise costs may arise. Amazon SQS operates on a pay-as-you-use model, which is cost-effective for predictable workloads but can be expensive at scale. Its transparent pricing structure benefits AWS-integrated organizations, allowing better expense management within the AWS framework.
Using Amazon SQS has led to increased productivity and reduced man-hour costs.
They meet their tasks effectively.
I want to receive good technical support, which I only need once a month or every six months, and the experience has been unsatisfactory.
There is plenty of community support available online.
The Apache community provides support for the open-source version.
I can easily scale up or down with Amazon SQS without any issues.
Amazon SQS is highly scalable, automatically managing itself based on the load.
Customers have not faced issues with user growth or data streaming needs.
With Amazon SQS, such maintenance is not needed, making it more reliable and secure.
The stability of Amazon SQS is very good, as I find it to be very stable.
Partitioning helps us distribute all the messages that we receive between all partitions, which helps us to be stable.
Apache Kafka is stable.
It would be beneficial if there was a provision to configure and retain messages for longer than a week.
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
We are always trying to find the best configs, which is a challenge.
Scaling up continues to be a challenge, though it is much easier now than it was in the beginning.
On a scale of one to ten, where one is very cheap, I would rate the pricing as one.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
If there's a failure in the system after consuming a message, SQS's settings ensure the message is not deleted until confirmation.
If we compare with other solutions such as RabbitMQ for messaging, Amazon SQS is easier to use and easier to create the queue.
Apache Kafka is particularly valuable for managing high levels of transactions.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
The impact of Apache Kafka's scalability features on my organization and data processing capabilities depends on how many messages each company wants to receive.
| Product | Market Share (%) |
|---|---|
| Amazon SQS | 7.8% |
| IBM MQ | 25.5% |
| ActiveMQ | 25.1% |
| Other | 41.6% |
| Product | Market Share (%) |
|---|---|
| Apache Kafka | 3.7% |
| Apache Flink | 14.8% |
| Databricks | 12.5% |
| Other | 69.0% |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 4 |
| Large Enterprise | 14 |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 18 |
| Large Enterprise | 47 |
Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating message oriented middleware, and empowers developers to focus on differentiating work. Using SQS, you can send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available. Get started with SQS in minutes using the AWS console, Command Line Interface or SDK of your choice, and three simple commands.
SQS offers two types of message queues. Standard queues offer maximum throughput, best-effort ordering, and at-least-once delivery. SQS FIFO queues are designed to guarantee that messages are processed exactly once, in the exact order that they are sent.
Apache Kafka is an open-source distributed streaming platform that serves as a central hub for handling real-time data streams. It allows efficient publishing, subscribing, and processing of data from various sources like applications, servers, and sensors.
Kafka's core benefits include high scalability for big data pipelines, fault tolerance ensuring continuous operation despite node failures, low latency for real-time applications, and decoupling of data producers from consumers.
Key features include topics for organizing data streams, producers for publishing data, consumers for subscribing to data, brokers for managing clusters, and connectors for easy integration with various data sources.
Large organizations use Kafka for real-time analytics, log aggregation, fraud detection, IoT data processing, and facilitating communication between microservices.
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